Aboul Ella Hassanien (Abo)

Powdery mildew caused by Erysiphe graminis f. sp. tritici is one of the most harmful disease causing great losses in wheat yield. Currently, thermal spectral sensing of plant disease under different environmental conditions in field is a cutting-edge research. Objectives of this study were to assess thermal imaging of normal and infected leaves for early detection of powdery mildew in wheat after the artificial infection with Erysiphe graminis fungus in a pot experiment under greenhouse conditions. Pot experiment lasting for 30 days was conducted. Additionally, wheat seedlings were artificially infected with pathogen at 10 days from sowing. This is the first study in Egypt to use thermal imaging technique for early detection of powdery mildew disease on leaf using thermal signatures of artificial infected leaves as a reference images. Particularly, the variations in temperature between infected and healthy leaves of wheat and the variation between air and leaf-surface temperatures under greenhouse conditions were sensed for early detection of disease. Results revealed that infection with powdery mildew pathogen induced changes in leaf temperature (from 0.37 °C after one hour from the infection to 0.78 °C at 21 days after infection with the pathogen) and metabolism, contributing to a distinct thermal signature characterizing the early and late phases of the infection.

This paper intends to enhance the image for the next usage
of region growing technique for segmenting the region of liver away from
other organs. The approach depends on a preprocessing phase to enhance
the appearance of the boundaries of the liver. This is performed using
contrast stretching and some morphological operations to prepare the
image for next segmentation phase. The approach starts with combining
Otsu's global thresholding with dilation and erosion to remove image
annotation and machine's bed. The second step of image preparation
is to connect ribs, and apply lters to enhance image and deepen liver
boundaries. The combined lters are contrast stretching and texture l-
ters. The last step is to use a simple region growing technique, which has
low computational cost, but ignored for its low accuracy. The proposed
approach is appropriate for many images, where liver could not be sep-
arated before, because of the similarity of the intensity with other close
organs. A set of 44 images taken in pre-contrast phase, were used to test
the approach. Validating the approach has been done using similarity
index. The experimental results, show that the overall accuracy oered
by the proposed approach results in 91.3% accuracy.

The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.